Many classical approaches are explored to solve inpainting problems, which can be grouped as diffuision-based [1]} and patch-based [2]} methods. Unfortunately, these methods are typically restricted to low-level image features, and hard to generate expressive results with large holes. Learning based methods equipped with GANs achieve great success in image inpainting [3]}, [4]}, [5]}, [6]}.
Concretely, Pathak et al. [3]} show that the adversarial training is critical to generate clear results for the inpainting. Furthermore, many bespoke inpainting convolutions are designed [8]}, [9]}, [10]} to gracefully model generalized features for masked regions. Moreover, the attention mechanism is also well explored in the inpainting [11]}, [9]}, [13]}, which takes long-range relation and aggregates reliable features from unmasked regions to masked ones.
Recently, StyleGAN [14]} based co-modulated inpainting models [5]}, [6]} have achieved high-quality generations enhanced with the style learning. Nevertheless, they [5]}, [6]} are quite likely to hallucinate incompatible image content over the mask. Although these pioneering works have tackled various problems in inpainting, they are generally incapable of reconstructing structural priors to maintain the faithful inpainting results.
